POS0403 SENSITIVITY OF AUTOMATED, U-NET-BASED SEGMENTATION TO LAMINAR CARTILAGE TRANSVERSE RELAXATION TIME (T2) IN KNEES WITH DIFFERENCES IN CONTRALATERAL OSTEOARTHRITIS STATUS – DATA FROM THE THE OA-BIO CONSORTIUM
Authors: F. Eckstein, A. Wisser, F. Roemer, F. Berenbaum, J. Kemnitz, G. Duda, S. Maschek, W. Wirth
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Radiographically normal knees with contralateral (CL) radiographic joint space narrowing (JSN) are at elevated risk of incident radiographic osteoarthritis (ROA). We previously observed increased superficial femorotibial cartilage layer transverse relaxation time (T2) on magnetic resonance images (MRI) of 39 KLG0 knees (0=normal) with advanced ROA in the contralateral knee (CL JSN), compared with 39 (1:1-matched) KLG0 knees without evidence of CL ROA (bilateral KLG0) [1]. These results suggest that cartilage matrix degeneration occurs in radiographically normal knees with CL JSN, and can be detected in vivo using MRI [1]. Application to larger cohorts, however, will benefit from fully automated image segmentation, as manual segmentation is a labor-intensive process. <h3>Objectives</h3> To evaluate the performance of U-Net-based cartilage segmentation, using an artificial intelligence (AI), i.e. convolutional neuronal network (CNN) approach, combined with fully automated detection of bony landmarks and the specific MRI slices requiring cartilage segmentation. The U-Net results were compared with those from manual segmentation, and the fully automated technology was applied to a larger (extended) control group. <h3>Methods</h3> U-Nets were trained from manual, quality-controlled cartilage segmentations in sagittal MESE images of the Osteoarthritis Initiative healthy reference cohort (n=92; HRC), one on the medial (MFTC) and one on the lateral femorotibial compartment (LFTC). All 7 echos (10-70 ms) were used. A 3rd U-Net was trained from manual, quality-controlled bone segmentations in 60 OAI HRC knees. The latter was used for automated detection of the weight-bearing femoral region of interest. The automated bone segmentation was registered to an atlas comprising both bone and cartilage segmentation, for identifying the slices required for cartilage segmentation. Automated post-processing was employed to correct obvious segmentation errors. This pipeline was first applied to n=39 dataset pairs (KLG0 with CL JSN vs. bilateral KLG0 knees) [1]. Then it was applied to n=642 bilateral KLG0 knees, to extend the limited paired case-control design (n=39) to a much larger control cohort. The agreement of the U-Net-based vs. manual segmentation was evaluated using the Dice Similarity Coefficient (DSC). Actual differences in cartilage T2 between a) 39 case vs. 39 control knees were compared between manual [1] vs. fully automated segmentation, and b) 39 case vs. 642 control knees (U-Net). Cohen's D was used as a measure of effect size. <h3>Results</h3> The DSC for the 2x39 knees with manual segmentations available ranged from 0.83±0.05 to 0.87±0.04 across the medial/lateral tibia and femur. When applied to the 39 pairs of case vs. control knees, fully automated segmentation identified differences in superficial layer T2, with the effect size apparently larger (Cohen's D MFTC/LFTC: 0.62/0.50) than that obtained from manual segmentation (0.46/0.48). Further, the U-Net was more sensitive to deep layer T2 differences (MFTC/LFTC: 0.36/0.50) than manual segmentation (0.25/0.35). When comparing the case knees to 642 control knees, the fully automated segmentation showed similar between-group differences (0.57/0.48 for superficial T2). <h3>Conclusion</h3> Fully automated segmentation of the femorotibial cartilage, in combination with automated detection of the relevant slices and femoral region of interest, showed high agreement with manual segmentation. The pipeline was able to reproduce differences in laminar T2 observed by manual segmentations [1], indicating early superficial cartilage matrix degeneration in radiographically normal knees with advanced contralateral radiographic KOA (JSN). The proposed fully automated analysis pipeline was readily expanded to a much larger cohort and thus represents a promising tool for future clinical studies on early cartilage change. <h3>References</h3> [1]Wirth W, et al. Osteoarthritis Cartilage. 2019; 27:1663 <h3>Acknowledgements</h3> This project has received funding from the Eurostars-2 joint programme with co-funding from the European Union Horizon 2020 research and innovation programme. The local funding agency supporting this work in Germany is the project management agency DLR, which acts on behalf of the Federal Ministry of Education and Research, BMBF (OA-BIO Eurostars-2 project - E! 114932). <h3>Disclosure of Interests</h3> Felix Eckstein Shareholder of: Chondrometrics GmbH, Grant/research support from: European Union: OA-BIO Eurostars-2 project (E! 114932), Employee of: Chondrometrics GmbH, Anna Wisser Grant/research support from: European Union: OA-BIO Eurostars-2 project (E! 114932), Employee of: Chondrometrics GmbH, Frank Roemer: None declared, Francis Berenbaum Shareholder of: 4Moving Biotech, Grant/research support from: European Union: OA-BIO Eurostars-2 project (E! 114932), Employee of: 4Moving Biotech, Jana Kemnitz Employee of: Chondrometrics GmbH, Georg Duda: None declared, Susanne Maschek Shareholder of: Chondrometrics GmbH, Grant/research support from: European Union: OA-BIO Eurostars-2 project (E! 114932), Employee of: Chondrometrics GmbH, Wolfgang Wirth Shareholder of: Chondrometrics GmbH, Grant/research support from: European Union: OA-BIO Eurostars-2 project (E! 114932), Employee of: Chondrometrics GmbH.